Volume 6, Issue 1, March 2020, Page: 8-13
Entropy of Topological Space S and Evolution of Phase Space of Dynzmical Systems
Malkhaz Mumladze, Department of Education, Exact and Natural Sciences, Gori University, Gori, Georgia
Received: Apr. 26, 2020;       Accepted: Jun. 1, 2020;       Published: Jun. 15, 2020
DOI: 10.11648/j.ijmfs.20200601.12      View  169      Downloads  26
In this paper, we introduced the concept of pseudo-convex open covering of topological spaces and entropy for topological spaces with such covering. Entropy was previously defined only for open coverings of compact topological spaces. It is shown by examples that the classes of topological spaces for which the concept of entropy is defined is quite wide. A discrete random process describing the evolution of the phase space of closed dynamical systems is built. Two random processes are constructed, one in which the elements of the transition matrices depend on the first indices, and the second Markov`s process, one in which the elements of the transition matrices do not depend from the first indices. The construction of transition matrices is based on the fact that the probability of a change in the phase space of a system to another space is proportional to the entropy of this other space. Based on the concept of entropy of topological spaces and on the well-known construction of an infinite product of probability measures which is also probabilistic introduced the concept entropy of trajectory of evolution of phase space of system. Previously, the entropy of the trajectory was defined only for the motion of a structureless material point, in this article is defined entropy of trajectory of evolution of structured objects represented in the form of topological spaces. On the basis of the concept entropy of trajectory, a method is determined for finding the most probable trajectory of evolution of the phase space of a closed system.
Topological Space, Covering, Entropy, Random Process
To cite this article
Malkhaz Mumladze, Entropy of Topological Space S and Evolution of Phase Space of Dynzmical Systems, International Journal of Management and Fuzzy Systems. Vol. 6, No. 1, 2020, pp. 8-13. doi: 10.11648/j.ijmfs.20200601.12
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T. A Chapmen Lecture on Q- manifolds Moscow 1981 (in Russian).
J. L Doob Stochastic processes New- York -John Wiley& Sons, London- Chapman& Hall 1953.
J. Bell Infinite product measures. Departments of Mathematics, university of Toronto, May 10, 2015.
H. H. Schaefer topological vector spaces, Springer 2012.
Edwin H. Spanier Algebraic Topology, MeGRAWHILL BOOK COMPANY 1966.
V. I. Bogachev, Measure Theory Springer 2007.
R. Ash Probability and measure theory, Academic Press; 2 edition 1999.
Ya. G. Sinai, “On the Notion of Entropy of a Dynamical System,” Doklady of Russian Academy of Sciences, 1959.
T. Downarowicz Entropy in dynamical Systems, New Mathematical Monograph, Cambridge University Press, Cambridge 2011.
Dou Dou, Wen Huang, Kyewon Koh Park Entropy dimension of measure preserving systems cornell university arXiv: 1312.7225v2 [math. DS] 3 Jan 2014.
Sadahiro SaekA Proof of the Existence of Infinite Product Probability Measures, The American Mathematical Monthly, Vol. 103, No. 8, 1996.
P. BILLINGSLEY Probability and Measure, Third Edition The Universityof Chicago, A Wiley-Interscience Publication WILEY & JOHN SONS New York, Chichester, Brisbane, Toronto, Singapore 1995.
E. B. Dynkin and A. A. Yushkevich Markov Processes, English ed. Plenum Press, New York, 1969.
Ryszard Engelking General topology Heldermann Verlag, 1989.
Lei ZHANG Leijun LIU Wen LI Sparse Trajectory Prediction Method Based on Entropy Estimation IEICE TRANSACTIONS on Information and Systems Vol. E99-D No. 6. 2016.
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